实时评估数据价值，加速模型训练并识别有害样本。
LiveVal: Time-aware Data Valuation via Adaptive Reference Points
- 基于自适应参考点的动态评估机制，无缝集成梯度下降训练。
- 相比传统方法提速180倍，同时保持对有害数据的强检测能力。
- 适合需要高效数据筛选与训练优化的工业级模型部署场景。
时间感知的数据价值评估能提升训练效率和模型鲁棒性，因为早期发现有害样本可避免数月的无效计算。然而，现有方法依赖模型重训练或收敛假设，或无法捕捉长期训练动态。我们提出LiveVal，一种高效的时序感知数据估值方法，包含三个关键设计：1）与SGD训练无缝集成，实现高效的数据贡献监控；2）基于参考值的归一化估值，建立可靠的基准；3）自适应参考点选择，支持实时更新并优化内存使用。我们建立了LiveVal稳定性的理论保证，证明其估值有界且与优化进展方向一致。大量实验表明，LiveVal在不同模态和模型规模下均实现高效数据估值，相比传统方法提速180倍，同时保持稳健的检测性能。
原文摘要 · Abstract (English)
Time-aware data valuation enhances training efficiency and model robustness, as early detection of harmful samples could prevent months of wasted computation. However, existing methods rely on model retraining or convergence assumptions or fail to capture long-term training dynamics. We propose LiveVal, an efficient time-aware data valuation method with three key designs: 1) seamless integration with SGD training for efficient data contribution monitoring; 2) reference-based valuation with normalization for reliable benchmark establishment; and 3) adaptive reference point selection for real-time updating with optimized memory usage. We establish theoretical guarantees for LiveVal's stability and prove that its valuations are bounded and directionally aligned with optimization progress. Extensive experiments demonstrate that LiveVal provides efficient data valuation across different modalities and model scales, achieving 180 speedup over traditional methods while maintaining robust detection performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。